High Resolution Fluorescence Imaging of Cell Membranes and Membrane Associated Interactions
Bibliographic record
Abstract
Advanced methods of fluorescence microscopy with micrometre - nanometre resolution are described. Wide-field and laser scanning techniques are used to measure fluorescence markers in whole cells including intracellular membranes. In contrast, plasma membranes of living cells are assessed selectively by the evanescent electromagnetic field in Total Internal Reflection Fluorescence Microscopy (TIRFM). TIRFM methods permit to measure cell-substrate topology as well as non-radiative intermolecular energy transfer (FRET) with nanometre resolution. Membrane dynamics – including membrane stiffness and fluidity – is assessed by spectral imaging and time-resolved fluorescence anisotropy measurements of the membrane marker 6-dodecanoyl-2-dimethylamino naphthalene (laurdan). Stiffness increases while fluidity decreases with increasing temperature as well as with an increasing intracellular amount of cholesterol. In addition, plasma membranes are always stiffer than intracellular membranes and retain their stiffness even after cholesterol depletion. This behavior may have some impact on the cellular uptake and release of pharmaceutical agents. Cell-substrate topology is measured by variable-angle TIRFM using fluorescent dyes with specific location either in the cytoplasm (e.g. calcein) or in the plasma membrane (e.g. laurdan). Due to its tumor-localizing and photosensitizing properties, measurements of protoporphyrin IX PP IX) in close proximity to the plasma membrane are of particular interest. It turned out that cell-substrate distances generally decreased, whereas focal adhesions were maintained upon light exposure. This implies that light-induced detachment of cells from their substrate was not likely to occur. Total internal reflection fluorescence microscopy (TIRFM) and non-radiative energy transfer (FRET) measurements have been combined in order to examine co-localization and possible interactions of various proteins, e.g. the amyloid precursor protein (APP) and the b-site APP-cleaving enzyme (BACE), which play some role in the pathogenesis of Alzheimer's disease. Both proteins were found in close proximity inside the cells and at some larger distance in the plasma membrane. A high amount of cholesterol was favourable for co-localization. The examples depicted above prove the potential of fluorescence microscopy with high spatial, temporal and spectral resolution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".